Structural variants identified by Oxford Nanopore PromethION sequencing of the human genome

AbstractWe sequenced the Yoruban NA19240 genome on the long read sequencing platform Oxford Nanopore PromethION for benchmarking and evaluation of recently published aligners and structural variant calling tools. In this work, we determined the precision and recall, present high confidence and high sensitivity call sets of variants and discuss optimal parameters. The aligner Minimap2 and structural variant caller Sniffles are both the most accurate and the most computationally efficient tools in our study. We describe our scalable workflow for identification, annotation, and characterization of tens of thousands of structural variants from long read genome sequencing of an individual or population. By discussing the results of this genome we provide an approximation of what can be expected in future long read sequencing studies aiming for structural variant identification.

Download Full-text

Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2021.761791 ◽

2021 ◽

Vol 12 ◽

Author(s):

Davide Bolognini ◽

Alberto Magi

Keyword(s):

Variant Calling ◽

Research Report ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Factors Affecting ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Sequencing Studies ◽

Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

Download Full-text

A Long-Read Sequencing Approach for Direct Haplotype Phasing in Clinical Settings

International Journal of Molecular Sciences ◽

10.3390/ijms21239177 ◽

2020 ◽

Vol 21 (23) ◽

pp. 9177

Author(s):

Simone Maestri ◽

Maria Giovanna Maturo ◽

Emanuela Cosentino ◽

Luca Marcolungo ◽

Barbara Iadarola ◽

...

Keyword(s):

Diagnostic Testing ◽

Variant Calling ◽

Clinical Settings ◽

Sequencing Data ◽

Sequencing Platform ◽

Variant Discovery ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

Second Generation Sequencing

The reconstruction of individual haplotypes can facilitate the interpretation of disease risks; however, high costs and technical challenges still hinder their assessment in clinical settings. Second-generation sequencing is the gold standard for variant discovery but, due to the production of short reads covering small genomic regions, allows only indirect haplotyping based on statistical methods. In contrast, third-generation methods such as the nanopore sequencing platform developed by Oxford Nanopore Technologies (ONT) generate long reads that can be used for direct haplotyping, with fewer drawbacks. However, robust standards for variant phasing in ONT-based target resequencing efforts are not yet available. In this study, we presented a streamlined proof-of-concept workflow for variant calling and phasing based on ONT data in a clinically relevant 12-kb region of the APOE locus, a hotspot for variants and haplotypes associated with aging-related diseases and longevity. Starting with sequencing data from simple amplicons of the target locus, we demonstrated that ONT data allow for reliable single-nucleotide variant (SNV) calling and phasing from as little as 60 reads, although the recognition of indels is less efficient. Even so, we identified the best combination of ONT read sets (600) and software (BWA/Minimap2 and HapCUT2) that enables full haplotype reconstruction when both SNVs and indels have been identified previously using a highly-accurate sequencing platform. In conclusion, we established a rapid and inexpensive workflow for variant phasing based on ONT long reads. This allowed for the analysis of multiple samples in parallel and can easily be implemented in routine clinical practice, including diagnostic testing.

Download Full-text

SDip: A novel graph-based approach to haplotype-aware assembly based structural variant calling in targeted segmental duplications sequencing

10.1101/2020.02.25.964445 ◽

2020 ◽

Cited By ~ 3

Author(s):

David Heller ◽

Martin Vingron ◽

George Church ◽

Heng Li ◽

Shilpa Garg

Keyword(s):

Variant Calling ◽

Human Diseases ◽

Segmental Duplications ◽

Duplicated Genes ◽

Single Nucleotide ◽

Biologically Relevant ◽

Structural Variant ◽

Long Read

AbstractSegmental duplications are important for understanding human diseases and evolution. The challenge to distinguish allelic and duplication sequences has hindered their phased assembly as well as characterization of structural variant calls. Here we have developed a novel graph-based approach that leverages single nucleotide differences in overlapping reads to distinguish allelic and duplication sequences information from long read accurate PacBio HiFi sequencing. These differences enable to generate allelic and duplication-specific overlaps in the graph to spell out phased assembly used for structural variant calling. We have applied our method to three public genomes: CHM13, NA12878 and HG002. Our method resolved 86% of duplicated regions fully with contig N50 up to 79 kb and produced <800 structural variant phased calls, outperforming state-of-the-part SDA method in terms of all metrics. Furthermore, we demonstrate the importance of phased assemblies and variant calls to the biologically-relevant duplicated genes such as SMN1, SRGAP2C, NPY4R and FAM72A. Our phased assemblies and accurate variant calling specifically in duplicated regions will enable the study of the evolution and adaptation of various species.

Download Full-text

Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

10.1101/2021.05.29.446291 ◽

2021 ◽

Author(s):

Yilei Fu ◽

Medhat Mahmoud ◽

Viginesh Vaibhav Muraliraman ◽

Fritz J Sedlazeck ◽

Todd J Treangen

Keyword(s):

Human Genome ◽

Variant Calling ◽

Dual Mode ◽

Read Mapping ◽

Structural Variant ◽

Long Reads ◽

Oxford Nanopore ◽

Mutational Hotspots ◽

Long Read ◽

High Level

Background: Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely-used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hotspots reduces read alignment accuracy and impedes structural variant detection. Findings: We tested our hypothesis by implementing a read mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via e.g. minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long read mapper (NGMLR). In support of our hypothesis, we show Vulcan improves the alignments for Oxford Nanopore Technology (ONT) long-reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read mapping methods alone. Conclusions: Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes, resulting in improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan

Download Full-text

svviz: a read viewer for validating structural variants

10.1101/016063 ◽

2015 ◽

Cited By ~ 1

Author(s):

Noah Spies ◽

Justin M Zook ◽

Marc Salit ◽

Arend Sidow

Keyword(s):

Open Source ◽

High Throughput Sequencing ◽

Reference Genome ◽

Structural Variants ◽

Insert Size ◽

Reference Allele ◽

Sequencing Platform ◽

Mate Pair ◽

Oxford Nanopore ◽

Long Read

Visualizing read alignments is the most effective way to validate candidate SVs with existing data. We present svviz, a sequencing read visualizer for structural variants (SVs) that sorts and displays only reads relevant to a candidate SV. svviz works by searching input bam(s) for potentially relevant reads, realigning them against the inferred sequence of the putative variant allele as well as the reference allele, and identifying reads that match one allele better than the other. Reads are assigned to the proper allele based on alignment score, read pair orientation and insert size. Separate views of the two alleles are then displayed in a scrollable web browser view, enabling a more intuitive visualization of each allele, compared to the single reference genome-based view common to most current read browsers. The web view facilitates examining the evidence for or against a putative variant, estimating zygosity, visualizing affected genomic annotations, and manual refinement of breakpoints. An optional command-line-only interface allows summary statistics and graphics to be exported directly to standard graphics file formats. svviz is open source and freely available from github, and requires as input only structural variant coordinates (called using any other software package), reads in bam format, and a reference genome. Reads from any high-throughput sequencing platform are supported, including Illumina short-read, mate-pair, synthetic long-read (assembled), Pacific Biosciences, and Oxford Nanopore. svviz is open source and freely available from https://github.com/svviz/svviz.

Download Full-text

SVIM: Structural Variant Identification using Mapped Long Reads

10.1101/494096 ◽

2018 ◽

Cited By ~ 2

Author(s):

David Heller ◽

Martin Vingron

Keyword(s):

Single Molecule ◽

Simulated Data ◽

Structural Variants ◽

Human Phenotype ◽

Structural Variant ◽

Small Indels ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read

AbstractMotivationStructural variants are defined as genomic variants larger than 50bp. They have been shown to affect more bases in any given genome than SNPs or small indels. Additionally, they have great impact on human phenotype and diversity and have been linked to numerous diseases. Due to their size and association with repeats, they are difficult to detect by shotgun sequencing, especially when based on short reads. Long read, single molecule sequencing technologies like those offered by Pacific Biosciences or Oxford Nanopore Technologies produce reads with a length of several thousand base pairs. Despite the higher error rate and sequencing cost, long read sequencing offers many advantages for the detection of structural variants. Yet, available software tools still do not fully exploit the possibilities.ResultsWe present SVIM, a tool for the sensitive detection and precise characterization of structural variants from long read data. SVIM consists of three components for the collection, clustering and combination of structural variant signatures from read alignments. It discriminates five different variant classes including similar types, such as tandem and interspersed duplications and novel element insertions. SVIM is unique in its capability of extracting both the genomic origin and destination of duplications. It compares favorably with existing tools in evaluations on simulated data and real datasets from PacBio and Nanopore sequencing machines.Availability and implementationThe source code and executables of SVIM are available on Github: github.com/eldariont/svim. SVIM has been implemented in Python 3 and published on bioconda and the Python Package [email protected]

Download Full-text

Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

GigaScience ◽

10.1093/gigascience/giab063 ◽

2021 ◽

Vol 10 (9) ◽

Author(s):

Yilei Fu ◽

Medhat Mahmoud ◽

Viginesh Vaibhav Muraliraman ◽

Fritz J Sedlazeck ◽

Todd J Treangen

Keyword(s):

Human Genome ◽

Variant Calling ◽

Dual Mode ◽

Read Mapping ◽

Structural Variant ◽

Long Reads ◽

Oxford Nanopore ◽

Long Read ◽

High Level ◽

Improved Accuracy

Abstract Background Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection. Findings We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone. Conclusions Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.

Download Full-text

Dysgu: efficient structural variant calling using short or long reads

10.1101/2021.05.28.446147 ◽

2021 ◽

Author(s):

Duncan M Baird ◽

Kez Cleal

Keyword(s):

Structural Variation ◽

State Of The Art ◽

Variant Calling ◽

High Sensitivity ◽

Sequencing Technologies ◽

Long Reads ◽

Long Read ◽

Low Coverage ◽

Paired End Sequencing

Structural variation (SV) plays a fundamental role in genome evolution and can underlie inherited or acquired diseases such as cancer. Long-read sequencing technologies have led to improvements in the characterization of structural variants (SVs), although paired-end sequencing offers better scalability. Here, we present dysgu, which calls SVs or indels using paired-end or long reads. Dysgu detects signals from alignment gaps, discordant and supplementary mappings, and generates consensus contigs, before classifying events using machine learning. Additional SVs are identified by remapping of anomalous sequences. Dysgu outperforms existing state-of-the-art tools using paired-end or long-reads, offering high sensitivity and precision whilst being among the fastest tools to run. We find that combining low coverage paired-end and long-reads is competitive in terms of performance with long-reads at higher coverage values.

Download Full-text

Dual Isoform Sequencing Reveals a Multifaceted Transcriptional Architecture of a Prototype Baculovirus

10.21203/rs.3.rs-637036/v1 ◽

2021 ◽

Author(s):

Gábor Torma ◽

Dóra Tombácz ◽

Norbert Moldován ◽

Ádám Fülöp ◽

István Prazsák ◽

...

Keyword(s):

Protein Coding ◽

Rna Molecules ◽

Non Coding Rna ◽

Oxford Nanopore ◽

The Pacific ◽

Viral Genes ◽

Long Read ◽

Oxford Nanopore Technologies ◽

Overlapping Transcripts

Abstract In this study, we used two long-read sequencing (LRS) techniques, Sequel from the Pacific Biosciences and MinION from Oxford Nanopore Technologies, for the transcriptional characterization of a prototype baculovirus, Autographacalifornica multiple nucleopolyhedrovirus. LRS is able to read full-length RNA molecules, and thereby to distinguish between transcript isoforms, mono- and polycistronic RNAs, and overlapping transcripts. Altogether, we detected 875 transcripts, of which 759 are novel and 116 have been annotated previously. These RNA molecules include 41 novel putative protein coding transcript (each containing 5’-truncated in-frame ORFs), 14 monocistronic transcripts, 99 multicistronic RNAs, 101 non-coding RNA, and 504 length isoforms. We also detected RNA methylation in 12 viral genes and RNA hyper-editing in the longer 5’-UTR transcript isoform of ORF 19 gene.

Download Full-text

Genotyping structural variants in pangenome graphs using the vg toolkit

10.1101/654566 ◽

2019 ◽

Cited By ~ 7

Author(s):

Glenn Hickey ◽

David Heller ◽

Jean Monlong ◽

Jonas A. Sibbesen ◽

Jouni Sirén ◽

...

Keyword(s):

De Novo ◽

State Of The Art ◽

Effective Means ◽

Point Mutations ◽

Structural Variants ◽

Short Read ◽

Yeast Strains ◽

Sequencing Studies ◽

Long Read

AbstractStructural variants (SVs) remain challenging to represent and study relative to point mutations despite their demonstrated importance. We show that variation graphs, as implemented in the vg toolkit, provide an effective means for leveraging SV catalogs for short-read SV genotyping experiments. We benchmarked vg against state-of-the-art SV genotypers using three sequence-resolved SV catalogs generated by recent long-read sequencing studies. In addition, we use assemblies from 12 yeast strains to show that graphs constructed directly from aligned de novo assemblies improve genotyping compared to graphs built from intermediate SV catalogs in the VCF format.

Download Full-text